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Elucidating the Mechanisms of SA-4-1BBL Immunoprevention Through Advanced Informatics Approaches

GSE301392 Mus musculus (mouse) RNA-seq (gene expression) 62 samples Submitted 2026/06/24 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 62 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
62 samples: 18 conditions, mostly in triplicateGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Elucidating the Mechanisms of SA-4-1BBL-Mediated Cancer Immunoprevention Through Advanced Informatics ApproachesBiomolecules · 2026
Summary (written by the submitters)
Cancer immunoprevention leverages the immune system’s surveillance mechanisms to mitigate tumor development. Vaccines constituting a tumor antigen and an immune adjuvant are perceived immunoprevention modalities. However, relevant tumor antigens are unknown for non-viral cancers, which constitute the majority of human cancers. Our group has recently shown that SA-4-1BBL, a novel oligomeric recombinant agonist of CD137 costimulatory molecule, as a single agent, has robust immunoprevention efficacy against various tumor types.
Read the full summary ↓
In marked contrast, agonistic antibodies to CD137 lacked such function. This study employs advanced informatics approaches for analyzing bulk RNA sequencing (RNA-seq) transcriptomics data to elucidate the molecular mechanisms driving SA-4-1BBL’s immunoprevention efficacy. Mice were treated subcutaneously with SA-4-1BBL or an agonistic antibody and injection site (IS) tissue and draining lymph nodes (LN) were analyzed for differentially expressed genes (DEGs). Our findings reveal that SA-4-1BBL induces a compartmentalized and temporally dynamic immune program, characterized by early effector activation at IS and sustained immune regulation in draining LN. K-means clustering of 4,564 DEGs identified 8 functionally distinct clusters, with IS-enriched clusters (1 and 4) upregulating effector genes such as Cd28, Klra1, Cd4, Cd40, and Cd40l, supporting CD4+ T and NK cell activation. In contrast, LN-dominant clusters (2 and 5) were enriched for regulatory genes (Tnfaip3, Irf5, Col1a2), implicating roles in tissue remodeling and long-term immune memory. Compared to agonistic antibody, SA-4-1BBL elicited a more selective and durable activation of adaptive immune pathways, including TCR signaling, Th1/Th2 differentiation, and NK cytotoxicity, while avoiding systemic inflammation. Agonistic antibody, in contrast, activated broader innate inflammatory programs, including Toll-like receptor and neurodegeneration-linked pathways. IMPRes analysis revealed that SA-4-1BBL engages sequential immune-regulatory circuits centered on Stat1, Cd247, and Ifng, and modulates the CD151–TGF-β axis, offering novel mechanistic insight into its immune-balancing effects. These findings define a compartmentalized immune architecture that underlies SA-4-1BBL’s ability to provide safe, long-lasting cancer immunoprevention. Our study highlights the value of applying comprehensive informatics techniques in decoding the spatial and functional complexity of tissue-specific immune programming.
Published in
Elucidating the Mechanisms of SA-4-1BBL-Mediated Cancer Immunoprevention Through Advanced Informatics Approaches
Verma M, Arguc FN, Malik MT et al. · Biomolecules 2026 · PMID 41750322 · doi:10.3390/biom16020252
This dataset
Open GSE301392 in finder → Find more mouse RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE301392_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1284907 and SRA study SRP596520. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41750322) ↗
Study design
18 conditions, mostly in triplicate
D3_2X_3H3_IS ×4 D3_2X_3H3_LN ×4 D3_2X_SA-4-1BBL_IS ×4 D3_2X_SA-4-1BBL_LN ×4 D7_2X_3H3_IS ×4 D7_2X_3H3_LN ×4 D7_2X_SA-4-1BBL_IS ×4 D7_2X_SA-4-1BBL_LN ×4 +10 more

Supports a between-group comparison across 62 samples.

How we read this: 18 replicated groups read from 62 sample titles; they account for 62 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 22 more — browse all 62 samples with per-sample file links →

Datasets on a similar topic

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